As ignored by most existing methods, the explanation for metric learning is important for a wide range of applications [1]}, [2]}, e.g. weakly supervised localization. In this work, we further shows its potential significance on diagnosis of metric learning losses and new applications including cross-view pattern discovery and interactive retrieval for person re-identification and face recognition. Gradient-based methods, e.g. guided BP [3]} can be adopted to metric learning, but recent works [4]}, [5]} claim that gradient can be irrelevant to the model and guided-BP fails on the sanity check [4]}. Among methods that pass the sanity check, Grad-CAM has been used for visualization of image retrieval in [7]}, but no quantitative result is provided in the paper. Instead of adding more gradient heuristics like Grad-CAM++ [8]}, we propose to explain the result of Grad-CAM under the decomposition framework. As shown in Section , by removing the heuristic step in Grad-CAM, the meaning of the generated activation map can be clearly explained using the proposed framework.
